Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization

This paper proposes an observer-based fuzzy adaptive output feedback control scheme for a class of uncertain single-input and single-output (SISO) nonlinear stochastic systems with quantized input signals and arbitrary switchings. The SISO system under consideration contains completely unknown nonlinear functions, unmeasured system states and quantized input signals quantized by a hysteretic quantizer. By adopting a new nonlinear disposal of the quantized input, the relationship between the control input and the quantized input is established. The hysteretic quantizer that we take can effectively avoid the chattering phenomena. Furthermore, the introduction of a linear observer makes the estimation of the states possible. Based on the universal approximation ability of the fuzzy logic systems (FLSs) and backstepping recursive design with the common stochastic Lyapunov function approach, a quantized output feedback control scheme is constructed, where the dynamic surface control (DSC) is explored to alleviate the computation burden. The proposed control scheme cannot only guarantee the boundedness of signals but also make the output of the system converge to a small neighborhood of the origin. The simulation results are exhibited to demonstrate the validity of the control scheme.     

Decentralized adaptive tracking quantized control for interconnected pure feedback time delay nonlinear systems

This paper focuses on the problem of adaptive tracking quantized control for a class of interconnected pure feedback time delay nonlinear systems. To satisfy the requirement of prescribed performance on the output tracking error, a novel asymmetric tangent barrier Lyapunov function is developed. The decentralized adaptive controller is designed via backstepping method. To deal with the uncertain interconnected nonlinear functions, we design a new virtual control input in the first step. Instead of estimating the bound of each unknown function, we use the adaptive method to estimate the bound of the composite function which is composed of the unknown functions. Thus the over parameterization problem is avoided. It is proved that the output of each subsystem satisfies the prescribed performance requirement and other state variables are bounded. Finally, the simulations are performed and the results verify the effectiveness of the proposed method.     

Event-triggered adaptive tracking control of uncertain nonlinear systems with prescribed performance and actuator failures

This article studies adaptive prescribed performance tracking control problem for a class of strict-feedback nonlinear systems with parametric uncertainties and actuator failures. Firstly, in order to compensate the multiple uncertainties and eliminate the influence of actuator failure, a new adaptive tracking controller based on first-order filter technology will be proposed, which simplifies the algorithm design process. Then, by introducing an asymmetric state transition function, the transient and steady performances of the output tracking error are both constrained such that the predetermined performance control goal is achieved. Moreover, to reduce the communication burden from the controller to the actuator, the event-triggered mechanism is designed, and there will be no Zeno phenomenon. Based on Lyapunov stability theory, it is strictly proved that output signal can track the reference signal and all the signals of the closed-loop system are bounded. Finally, a simulation example is performed and the results demonstrate effectiveness of the proposed strategy.     

Adaptive fuzzy asymptotically tracking control of full state constrained nonlinear system based on a novel Nussbaum-type function

This study carries out the problem of adaptive backstepping fuzzy tracking control for a class of full state constrained uncertain nonlinear system with unknown control directions. Based on Nussbaum-type functions and tan-type Barrier Lyapunov functions, a novel adaptive fuzzy tracking controller is proposed to guarantee that the system output tracking error asymptotically converges to zero, while the constraints on the states of system will not be violated during operation. Compared with the existing results, a better convergence effect is obtained for this class of systems. Stability analysis of the proposed closed-loop control system is supported by the Lyapunov stability theory. Finally, a simulation example is presented to illustrate the effectiveness of the proposed control strategy.     

Quantized adaptive tracking control for nonlinear systems with actuator backlash compensation

In this paper, the problem of adaptive tracking control is investigated for nonlinear systems with asymmetric actuator backlash. We assume that the nonlinearities of the systems are unknown and the external disturbances are bounded. First, the control input will be quantized by a hysteresis-type quantizer, which can reduce the communication rate of the control signal. Then, the asymmetric actuator backlash is approximated to a new model, and a novel adaptive controller with the quantizer is designed via an adaptive backstepping technique to guarantee all the signals of the closed-loop tracking error system are uniform ultimate boundedness. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed algorithm.     

Adaptive tracking control for quantized nonlinear systems via backstepping design technique

In this paper, the adaptive tracking control problem for a class of strict-feedback nonlinear systems with quantized input signal is investigated. The hysteretic quantizer is introduced to avoid the chattering phenomenon and the backstepping method is used to design the controller. The tracking errors are guaranteed to be bounded in a small neighborhood of zero via appropriate design parameters. Finally, two simulation examples are given, and the simulation results further demonstrate the effectiveness of the proposed method.     

Error-driven nonlinear feedback-based fuzzy adaptive output dynamic surface control for nonlinear systems with partially constrained tracking errors

In this paper, a novel error-driven nonlinear feedback technique is designed for partially constrained errors fuzzy adaptive observer-based dynamic surface control of a class of multiple-input-multiple-output nonlinear systems in the presence of uncertainties and interconnections. There is no requirements that the states are available for the controller design by constructing fuzzy adaptive observer, which can online identify the unmeasurable states using available output information only. By transforming partial tracking errors into new error variables, partially constrained tracking errors can be guaranteed to be confined in pre-specified performance regions. The feature of the error-driven nonlinear feedback technique is that the feedback gain self-adjusts with varying tracking errors, which prevents high-gain chattering with large errors and guarantees disturbance attenuation with small errors. Based on a new non-quadratic Lyapunov function, it is proved that the signals in the resulted closed-loop system are kept bounded. Simulation and comparative results are given to demonstrate the effectiveness of the proposed method.     

Global practical tracking for nonlinear systems with uncertain dead-zone input via output feedback

In this paper, global practical tracking is investigated via output feedback for a class of uncertain nonlinear systems subject to unknown dead-zone input. The nonlinear systems under consideration allow more general growth restriction, where the growth rate includes unknown constant and output polynomial function. Without the precise priori knowledge of dead-zone characteristic, an input-driven observer is designed by introducing a novel dynamic gain. Based on non-separation principle, a universal adaptive output feedback controller is proposed by combining dynamic high-gain scaling approach with backstepping method. The controller proposed guarantees that the closed-loop output can track any smooth and bounded reference signal by any small pre-given tracking error, while all closed-loop signals are globally bounded. Finally, simulation examples are given to illustrate the effectiveness of our dynamic output feedback control scheme.     

Distributed adaptive observer-based prescribed finite-time control of constrained multi-agent systems

This work considers a distributed adaptive output feedback control problem for nonlinear constrained multi-agent systems (MAS) in the prescribed finite time. To begin with, a state observer is constructed to estimate the unmeasurable state. Then, we develop a novel observer based distributed adaptive prescribed finite time output feedback control algorithm by incorporating the prescribed finite-time control technique into the backstepping design method. Through Lyapunov stability theory, it can be shown that all signals of MASs are bounded, the tracking errors converge to the adjustable regions around the origin within the pre-given error accuracy and settling time, and all states keep in the prescribed constraint regions. Finally, a simulation example verifies the efficacy of the obtained theoretical results.     

Event-triggered decentralized output-feedback control for interconnected nonlinear systems with input quantization

In this paper, the event-triggered decentralized control problem for interconnected nonlinear systems with input quantization is investigated. A state observer is constructed to estimate the unmeasurable states, and the state-dependent interconnections are accommodated by presenting some smooth functions. Then by employing backstepping technique and neural networks (NNs) approximation capability, a novel decentralized output feedback control strategy and an event-triggered mechanism are designed simultaneously. It is proved through Lyapunov theory that the closed-loop system is stable and the tracking property of all subsystems is guaranteed. Finally, the effectiveness of the proposed scheme is illustrated by an example.     

Backstepping-based decentralized adaptive neural H∞ tracking control for a class of large-scale nonlinear interconnected systems

The decentralized tracking control methods for large-scale nonlinear systems are investigated in this paper. A backstepping-based robust decentralized adaptive neural H_∞ tracking control method is addressed for a class of large-scale strict feedback nonlinear systems with uncertain disturbances. Under the condition that the nonlinear interconnection functions in subsystems are unknown and mismatched, the decentralized adaptive neural network H_∞ tracking controllers are designed based on backstepping technology. Neural networks are used to approximate the packaged multinomial including the unknown interconnections and nonlinear functions in the subsystems as well as the derivatives of the virtual controls. The effect of external disturbances and approximation errors is attenuated by H_∞ tracking performance. Whether the external disturbances occur or not, the output tracking errors of the close-loop system are guaranteed to be bounded. A practical example is provided to show the effectiveness of the proposed control approach.     

A new prescribed performance control approach for uncertain nonlinear dynamic systems via back-stepping

Though traditional prescribed performance control (PPC) schemes can guarantee tracking errors with desired transient performance, they cannot ensure the convergence of tracking errors with small overshoot. In this study, we propose a novel PPC methodology for a class of uncertain nonlinear dynamic systems based on back-stepping, guaranteeing output tracking with small (even zero) overshoot. Firstly, new performance functions are constructed to constrain tracking errors. Then, to facilitate control designs, the “constrained” systems are transformed into equivalent “unconstrained” ones by designing a series of transformed errors. Furthermore, robust back-stepping controllers, requiring no priori knowledge of uncertainties’ upper bounds, are developed utilizing transformed errors instead of initial tracking errors. Semi-globally uniformly bounded stability of the closed-loop control system is guaranteed via Lyapunov synthesis. Finally, simulation and experiment results are presented to verify the design.     

Consensus tracking control for nonlinear multiagent systems with asymmetric state constraints and input delays

In this paper, the consensus tracking problem is studied for a group of nonlinear heterogeneous multiagent systems with asymmetric state constraints and input delays. Different from the existing works, both input delays and asymmetric state constraints are assumed to be nonuniform and time-varying. By introducing a nonlinear mapping to handle the problem caused by state constraints, not only the feasibility condition is removed, but also the restriction on the constraint boundary functions is relaxed. The time-varying input delays are compensated by developing an auxiliary system. Furthermore, by utilizing the dynamic surface control method, neural network technology and the designed finite-time observer, the distributed adaptive control scheme is developed, which can achieve the synchronization between the followers’ output and the leader without the violation of full-state constraints. Finally, a numerical simulation is provided to verify the effectiveness of the proposed control protocol.     

Non-fragile quantized H∞ output feedback control for nonlinear systems with quantized inputs and outputs

This paper is concerned with non-fragile $H_{\infty }$ control problems for a class of continuous-time nonlinear systems with unknown nonlinearity and quantized inputs and outputs. The construction of both static output feedback (SOF) and observer-based output feedback (OBOF) control laws in the presence of additive interval-bounded controller coefficient variations can be divided into two parts, linear and nonlinear parts. The linear part plays a role in achieving the $H_{\infty }$ performance, while the nonlinear part is used to reduce the quantization effect. However, it should be pointed out that the effect of input and output quantization can be eliminated fully for SOF case by requiring knowledge of all signs of the states, but only the effect of input quantization can be eliminated for OBOF case. It is worth mentioning that three novel alternative methods with strict linear matrix inequality (LMI) conditions are proposed to design both SOF and OBOF controllers. In particular, these three new methods do not introduce any other auxiliary constraints as many existing results do where a matrix equality constraint between system matrix and Lyapunov matrix is often inserted. Finally, the effectiveness and advantages of the proposed control methods are demonstrated by a numerical example.     

Observer-based decentralized adaptive control for large-scale nonlinear systems with mixed dynamic interaction

The problem of decentralized adaptive control is investigated for a class of large-scale nonstrict-feedback nonlinear systems subject to dynamic interaction and unmeasurable states, where the dynamic interaction is related to both input and output items. First, the fuzzy logic system is utilized to tackle unknown nonlinear function with nonstrict-feedback structure. Then, by combining adaptive and backstepping technology, the proper output feedback controller is designed. Meanwhile, a fuzzy state observer is proposed to estimate the unmeasurable states. The proposed controller could guarantee that all the signals of the resulting closed-loop systems are bounded. Finally, the applicability of the proposed controller is well carried out by a simulation example.     

Robust distributed tracking control for linear multi-agent systems based on distributed intermediate estimator

This paper considers the distributed tracking control problem for linear multi-agent systems with disturbances and a leader whose control input is nonzero and not available to any follower. Based on the relative output measurements of neighboring agents, a novel distributed observer-based tracking protocol is proposed, where the distributed intermediate estimators are constructed to estimate the leader’s unknown control input and the states of the tracking error system simultaneously, then a distributed tracking protocol is designed based on the derived estimates. It is proved that the states of the tracking error system are uniformly ultimately bounded and an explicit tracking error bound is obtained. A simulation example of aircrafts verifies the effectiveness of the proposed method.     

Terminal iterative learning control for discrete-time nonlinear systems based on neural networks

The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.     

Dynamic event-triggered output consensus of nonstrict-feedback MASs with predefined performance

This paper deals with the output consensus problem for uncertain nonstrict-feedback leader-follower multi-agent systems with predefined performance. A distributed event-triggered control strategy with dynamic threshold is proposed to update the actual control input and alleviate the computation burden of the communication procedure effectively. The unknown nonstrict-feedback structures are addressed by using the property of radial basis function neural networks. It is worth noting that in practical applications, the predefined performance often alternates between constrained and unconstrained cases in some extreme situations. To overcome this challenge, a novel coordinate transformation technique is incorporated to tackle both the two cases with and without performance constraint in a unified manner. As a result, the proposed event-triggered control approach ensures that the output consensus errors converge to zero asymptotically, and all the signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed protocol is demonstrated by the simulation results.     

Fuzzy adaptive fault-tolerant tracking control of MIMO stochastic pure-feedback nonlinear systems with actuator failures

This paper studies the adaptive fuzzy fault-tolerant control design problem for a class of stochastic multi-input and multi-output (MIMO) nonlinear systems in pure-feedback form. The nonlinear systems under study contain unknown functions, unmeasured states and actuator faults, which are described by the loss of effectiveness and lock-in-place modes. With the help of fuzzy logic systems identifying uncertain stochastic nonlinear systems, a fuzzy state observer is established for estimating the unmeasured states. Based on the backstepping design technique with the nonlinear tolerant-fault control theory, an adaptive fuzzy output feedback faults-tolerant control approach is developed. It is proved that the proposed fault-tolerant control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability. Moreover, the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach.